An Improved Set of Design Criteria for Slotted Liners in Steam Assisted Gravity Drainage Operation
Bibliographic record
Abstract
Slotted liners are widely used in steam-assisted gravity drainage (SAGD) wells to control sand production and sustain wellbore productivity. The slotted liner can provide desirable performance when appropriately designed. A literature review indicates a limited number of studies that offer design criteria specifically for SAGD wells. Moreover, past criteria seem to neglect some key factors, which may lead to inadequate slot design. This paper proposes a set of graphical design criteria for slotted liners in SAGD production wells, using prepacked sand retention testing (SRT) data. The SRT is designed to incorporate several essential factors that are not present in the past design criteria, such as slot density, steam breakthrough, and particle size distribution (PSD). The proposed design criteria are presented graphically for normal and aggressive conditions, where the aggressive condition accounts for the potential occurrence of the steam breakthrough. It is found that the upper bound of the design window is substantially lower for the aggressive condition due to the higher sand production after the steam breakthrough. The design criteria also indicate that the slotted liner is suitable only for the formations with low fines content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".